Euler’s Elastica Strategy for Limited-angle Computed Tomography Image Reconstruction

Zhang, Hanming; Wang, Linyuan; Duan, Yuping; Li, Lei; Hu, Guoen; Yan, Bin

doi:10.1109/tns.2017.2717864

Cited by 8 publications

(15 citation statements)

References 35 publications

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“…N 0 was set as 5 9 10 5 in reference to the literature. 47 The scanning parameter of digital phantom is consistent with that of training datasets. DECT images are reconstructed using the FBP algorithm, as shown in Fig.…”

Section: Discussionsupporting

confidence: 62%

“…Noises are added to the dual‐energy projections, which are generated using a Poisson model as follows:

N = Poisson (N_{0} exp (- p)),

where

N_{0}

refers to the number of incident x‐ray photons, and

p

denotes the measured number of photons in the projection.

N_{0}

was set as 5 × 10 5 in reference to the literature . The scanning parameter of digital phantom is consistent with that of training datasets.…”

Section: Discussionmentioning

confidence: 99%

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Image domain dual material decomposition for dual‐energy CT using butterfly network

Zhang

Wang

et al. 2019

Medical Physics

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Purpose Dual‐energy CT (DECT) has been increasingly used in imaging applications because of its capability for material differentiation. However, material decomposition suffers from magnified noise from two CT images of independent scans, leading to severe degradation of image quality. Existing algorithms exhibit suboptimal decomposition performance because they fail to fully depict the mapping relationship between DECT images and basis materials under noisy conditions. Convolutional neural network exhibits great promise in the modeling of data coupling and has recently become an important technique in medical imaging application. Inspired by its impressive potential, we developed a new Butterfly network to perform the image domain dual material decomposition. Methods The Butterfly network is derived from the model of image domain DECT decomposition by exploring the geometric relationship between the mapping functions of the data model and network components. The network is designed as the double‐entry double‐out crossover architecture based on the decomposition formulation. It enters a pair of dual‐energy images as inputs and defines the ground true decomposed images as each label. The crossover architecture, which plays an important role in material decomposition, is designed to implement the information exchange between the two material generation pathways in the network. The proposed network is further applied on the digital phantom and clinical data to evaluate its performance. Results The qualitative and quantitative evaluations of the material decomposition of digital phantoms and clinical data indicate that the proposed network outperforms its counterparts. For the digital phantom, the proposed network reduces the standard deviation (SD) of noise in tissue, bone, and mixture regions by an average of 95.75% and 86.58% compared with the direct matrix inversion and the conventional iterative method, respectively. The line profiles and image biases of the decomposition results of digital phantom indicate that the proposed network provides the decomposition results closest to the ground truth. The proposed network reduces the SD of the noise in decomposed images of clinical head data by over 90% and 80% compared with the direct matrix inversion and conventional iterative method, respectively. As the modulation transfer function decreases to 50%, the proposed network increases the spatial resolution by average factors of 1.34 and 1.17 compared with the direct matrix inversion and conventional iterative methods, respectively. The proposed network is further applied to the clinical abdomen data. Among the three methods, the proposed method received the highest score from six radiologists in the visual inspection of noise suppression in the clinical data. Conclusions We develop a model‐based Butterfly network to perform image domain material decomposition for DECT. The decomposition results of digital phantom validate its capability of decomposing two basis materials from DECT images. The proposed approach also le...

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Section: Discussionsupporting

confidence: 62%

“…Noises are added to the dual‐energy projections, which are generated using a Poisson model as follows:

N = Poisson (N_{0} exp (- p)),

where

N_{0}

refers to the number of incident x‐ray photons, and

p

denotes the measured number of photons in the projection.

N_{0}

was set as 5 × 10 5 in reference to the literature . The scanning parameter of digital phantom is consistent with that of training datasets.…”

Section: Discussionmentioning

confidence: 99%

Image domain dual material decomposition for dual‐energy CT using butterfly network

Zhang

Wang

et al. 2019

Medical Physics

Self Cite

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“…Typically, we can express the pipeline for such This has seen much success in heavy metal artefact reduction [22,23] where a regularisation functional for the inpainting problem may be constructed from dictionary learning [24], fractional order TV [23], and Euler's Elastica [25]. Euler's Elastica has also been used in the limited angle problem [26] along with more customised interpolation methods [27]. These approaches allow us to use prior knowledge on the sinogram to calculate v and then spatial prior knowledge to calculate u from v; at no point is the choice of v influenced by our spatial prior.…”

Section: Context and Proposed Modelmentioning

confidence: 99%

Directional sinogram inpainting for limited angle tomography

et al. 2019

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In this paper we propose a new joint model for the reconstruction of tomography data under limited angle sampling regimes. In many applications of Tomography, e.g. Electron Microscopy and Mammography, physical limitations on acquisition lead to regions of data which cannot be sampled. Depending on the severity of the restriction, reconstructions can contain severe, characteristic, artefacts. Our model aims to address these artefacts by inpainting the missing data simultaneously with the reconstruction. Numerically, this problem naturally evolves to require the minimisation of a non-convex and non-smooth functional so we review recent work in this topic and extend results to fit an alternating (block) descent framework. We perform numerical experiments on two synthetic datasets and one Electron Microscopy dataset. Our results show consistently that the joint inpainting and reconstruction framework can recover cleaner and more accurate structural information than the current state of the art methods.

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“…Different regularization terms lead to different models and different solutions. Image reconstruction models based on regularization generally assume that images are sparse in the total variation (TV) transformation domain [9], wavelet transform domain [10] and so on [11][12][13][14]. Sidky et al proposed the total variation (TV) minimization reconstruction algorithm in few-view and limited-angle problems to preserve the image edges and reduce the artifacts [9].…”

Section: Introductionmentioning

confidence: 99%

Guided Image Filtering Reconstruction Based on Total Variation and Prior Image for Limited-Angle CT

Shen

Gong

et al. 2020

IEEE Access

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For limited-angle computed tomography (CT) image reconstruction, the classical total variation (TV) based algorithms suffer from the limited-angle artifacts, because TV only used the gradient information of the image. The priori image constrained compressed sensing (PICCS) based reconstruction algorithms can reduce the limited-angle artifacts by using a priori image consistent with the target image. However, it is difficult to ensure the consistency of priori image and target image in practice. In order to reconstruct high quality image when the prior image is inconsistent with the target image, we proposed a guided image filter reconstruction based on TV and prior image (TVPI-G). In each iteration phase, our algorithm first performs a TV step (include simultaneous algebra reconstruction technique (SART) and TV) to get initiatory reconstruction image; Then, the results of TV iteration are combined with prior images to form an intermediate result; Finally, we use the guided image filter to modify the intermediate results with the TV result as the guide image. Numerical reconstruction results on simulation phantom with different intensities Poisson noise illustrates that our proposed TVPI-G algorithm is better than other comparison algorithms in both qualitative and quantitative aspects, including TV, PICCS, and SART guided image filtering.

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Euler’s Elastica Strategy for Limited-angle Computed Tomography Image Reconstruction

Cited by 8 publications

References 35 publications

Image domain dual material decomposition for dual‐energy CT using butterfly network

Image domain dual material decomposition for dual‐energy CT using butterfly network

Directional sinogram inpainting for limited angle tomography

Guided Image Filtering Reconstruction Based on Total Variation and Prior Image for Limited-Angle CT

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